OpenRaiser/Intern-Atlas huggingface dataset:
A Validation of the Proposed Component-Based Development Process
Venue: arXiv cs.SE 2012
Year: 2012

Component-based development (CBD) is a name, with which software development professionals are quite familiar. There are several models which have been proposed for CBD in last few years. They contain good features but there are some improvement possibilities in them. The objective of this paper is to propose a process for CBD and to evaluate the effects of quality parameters on reusability. The validations of the proposed CBD model provide positive indication for software (SW) industry that it can be successfully implemented for CBD projects.
A Framework for Next Generation Mobile and Wireless Networks Application Development using Hybrid Component Based Development Model
Venue: arXiv cs.SE 2012
Year: 2012

The IP Multimedia Subsystems (IMS) that features in Next Generation Networks (NGN) offers the application developer (third party) abilities to map out applications over mobile telecommunication infrastructure. The IMS comes about with APIs useful for mobile application developers to create applications to meet end-users' demands and comply with the provider's infrastructure set up at the same time. Session Initiation Protocol (SIP) is a signaling protocol for this architecture. It is used for establishing sessions in IP network, making it an ideal candidate for supporting terminal mobility in to deliver the services with improved Quality of Services (QOS). The realization of IMS's virtues as far as software design is concerned is faced by lack of standardizations and methodologies throughout application development process. In this paper, we report on progress on ongoing research by our group toward putting together a platform as a testbed used for NGN application development. We examine a novel component based development model used for SIP based mobile applications. The developed model is to be used as framework for general purpose application development over the testbed. We apply this model on MObile Mass EXamination (MOMEX) system that is an application attracting the interest of educational authorities around the world due to its potential convenience.
Novel Component Based Development Model For Sip-Based Mobile Application
Venue: arXiv cs.SE 2012
Year: 2012

Universities and Institutions these days' deals with issues related to with assessment of large number of students. Various evaluation methods have been adopted by examiners in different institutions to examining the ability of an individual, starting from manual means of using paper and pencil to electronic, from oral to written, practical to theoretical and many others. There is a need to expedite the process of examination in order to meet the increasing enrolment of students at the universities and institutes. Sip Based Mass Mobile Examination System (SiBMMES) expedites the examination process by automating various activities in an examination such as exam paper setting, Scheduling and allocating examination time and evaluation (auto-grading for objective questions) etc. SiBMMES uses the IP Multimedia Subsystem (IMS) that is an IP communications framework providing an environment for the rapid development of innovative and reusable services Session Initial Protocol (SIP) is a signalling (request-response) protocol for this architecture and it is used for establishing sessions in an IP network, making it an ideal candidate for supporting terminal mobility in the IMS to deliver the services, with the extended services available in IMS like open APIs, common network services, Quality of Services (QoS) like multiple sessions per call, Push to Talk etc often requiring multiple types of media (including voice, video, pictures, and text). SiBMMES is an effective solution for mass education evaluation using mobile and web technology. In this paper, a novel hybrid component based development (CBD) model is proposed for SiBMMES.
Semantic snippet construction for search engine results based on segment evaluation
Venue: arXiv cs.IR 2012
Year: 2012

The result listing from search engines includes a link and a snippet from the web page for each result item. The snippet in the result listing plays a vital role in assisting the user to click on it. This paper proposes a novel approach to construct the snippets based on a semantic evaluation of the segments in the page. The target segment(s) is/are identified by applying a model to evaluate segments present in the page and selecting the segments with top scores. The proposed model makes the user judgment to click on a result item easier since the snippet is constructed semantically after a critical evaluation based on multiple factors. A prototype implementation of the proposed model confirms the empirical validation.
Live-marker: A personalized web page content marking tool
Venue: arXiv cs.IR 2012
Year: 2012

The tremendous amount of increase in the quantity of information resources available on the web has made the total time that the user spends on a single page very minimal. Users revisiting the same page would be able to fetch the required information much faster if the information that they consumed during the previous visit(s) gets presented to them with a special style. This paper proposes a model which empowers the users to mark the content interesting to them, so that it can be identified easily during successive visits. In addition to the explicit marking by the users, the model facilitates implicit marking based on the user preferences. The prototype implementation based on proposed model validates the model's efficiency.
We.I.Pe: Web Identification of People using e-mail ID
Venue: arXiv cs.IR 2012
Year: 2012

With the phenomenal growth of content in the World Wide Web, the diversity of user supplied queries have become vivid. Searching for people on the web has become an important type of search activity in the web search engines. This paper proposes a model named "We.I.Pe" to identify people on the World Wide Web using e-mail Id as the primary input. The approach followed in this research work provides the collected information, based on the user supplied e-mail id, in an easier to navigate manner. The grouping of collected information based on various sources makes the result visualization process more effective. The proposed model is validated by a prototype implementation. Experiments conducted on the prototype implementation provide encouraging results
"Performance Evaluation of Wi-Fi comparison with WiMAX Networks"
Venue: arXiv cs.NI 2012
Year: 2012

Wireless networking has become an important area of research in academic and industry. The main objectives of this paper is to gain in-depth knowledge about the Wi-Fi- WiMAX technology and how it works and understand the problems about the WiFi- WiMAX technology in maintaining and deployment. The challenges in wireless networks include issues like security, seamless handover, location and emergency services, cooperation, and QoS.The performance of the WiMAX is better than the Wi-Fi and also it provide the good response in the access. It's evaluated the Quality of Service (Qos) in Wi-Fi compare with WiMAX and provides the various kinds of security Mechanisms. Authentication to verify the identity of the authorized communicating client stations. Confidentiality (Privacy) to secure that the wirelessly conveyed information will remain private and protected. Take necessary actions and configurations that are needed in order to deploy Wi-Fi -WiMAX with increased levels of security and privacy
Query Matrices for Retrieving Binary Vectors Based on the Hamming Distance Oracle
Venue: arXiv cs.DM 2012
Year: 2012

The Hamming oracle returns the Hamming distance between an unknown binary $n$-vector $x$ and a binary query $n$-vector y. The objective is to determine $x$ uniquely using a sequence of $m$ queries. What are the minimum number of queries required in the worst case? We consider the query ratio $m/n$ to be our figure of merit and derive upper bounds on the query ratio by explicitly constructing $(m,n)$ query matrices. We show that our recursive and algebraic construction results in query ratios arbitrarily close to zero. Our construction is based on codes of constant weight. A decoding algorithm for recovering the unknown binary vector is also described.
Efficient Relay Selection Scheme for Delay-Limited Non-Orthogonal Hybrid-ARQ Relay Channels
Venue: arXiv cs.IT 2012
Year: 2012

We consider a half-duplex wireless relay network with hybrid-automatic retransmission request (HARQ) and Rayleigh fading channels. In this paper, we analyze the outage probability of the multi-relay delay-limited HARQ system with opportunistic relaying scheme in decode-and-forward mode, in which the \emph{best} relay is selected to transmit the source's regenerated signal. A simple and distributed relay selection strategy is proposed for multi-relay HARQ channels. Then, we utilize the non-orthogonal cooperative transmission between the source and selected relay for retransmitting of the source data toward the destination if needed, using space-time codes or beamforming techniques. We analyze the performance of the system. We first derive the cumulative density function (CDF) and probability density function (PDF) of the selected relay HARQ channels. Then, the CDF and PDF are used to determine the outage probability in the $l$-th round of HARQ. The outage probability is required to compute the throughput-delay performance of this half-duplex opportunistic relaying protocol. The packet delay constraint is represented by $L$, the maximum number of HARQ rounds. An outage is declared if the packet is unsuccessful after $L$ HARQ rounds. Furthermore, closed-form upper-bounds on outage probability are derived and subsequently are used to investigate the diversity order of the system. Based on the derived upper-bound expressions, it is shown that the proposed schemes achieve the full spatial diversity order of $N+1$, where $N$ is the number of potential relays. Our analytical results are confirmed by simulation results.
Exploiting the `Web of Trust' to improve efficiency in collaborative networks
Venue: arXiv cs.SI 2012
Year: 2012

Maintaining high quality content is one of the foremost objectives of any web-based collaborative service that depends on a large number of users. In such systems, it is nearly impossible for automated scripts to judge semantics as it is to expect all editors to review the content. This catalyzes the need for trust-based mechanisms to ensure quality of an article immediately after an edit. In this paper, we build on previous work and develop a framework based on the `web of trust' concept to calculate satisfaction scores for all users without the need for perusing the article. We derive some bounds for systems based on our mechanism and show that the optimization problem of selecting the best users to review an article is NP-Hard. Extensive simulations validate our model and results, and show that trust-based mechanisms are essential to improve efficiency in any online collaborative editing platform.
Recommender System Based on Algorithm of Bicluster Analysis RecBi
Venue: arXiv cs.AI 2012
Year: 2012

In this paper we propose two new algorithms based on biclustering analysis, which can be used at the basis of a recommender system for educational orientation of Russian School graduates. The first algorithm was designed to help students make a choice between different university faculties when some of their preferences are known. The second algorithm was developed for the special situation when nothing is known about their preferences. The final version of this recommender system will be used by Higher School of Economics.
Maximum Multiflow in Wireless Network Coding
Venue: arXiv cs.IT 2012
Year: 2012

In a multihop wireless network, wireless interference is crucial to the maximum multiflow (MMF) problem, which studies the maximum throughput between multiple pairs of sources and sinks. In this paper, we observe that network coding could help to decrease the impacts of wireless interference, and propose a framework to study the MMF problem for multihop wireless networks with network coding. Firstly, a network model is set up to describe the new conflict relations modified by network coding. Then, we formulate a linear programming problem to compute the maximum throughput and show its superiority over one in networks without coding. Finally, the MMF problem in wireless network coding is shown to be NP-hard and a polynomial approximation algorithm is proposed.
Strong Scaling of Matrix Multiplication Algorithms and Memory-Independent Communication Lower Bounds
Venue: arXiv cs.DS 2012
Year: 2012

A parallel algorithm has perfect strong scaling if its running time on P processors is linear in 1/P, including all communication costs. Distributed-memory parallel algorithms for matrix multiplication with perfect strong scaling have only recently been found. One is based on classical matrix multiplication (Solomonik and Demmel, 2011), and one is based on Strassen's fast matrix multiplication (Ballard, Demmel, Holtz, Lipshitz, and Schwartz, 2012). Both algorithms scale perfectly, but only up to some number of processors where the inter-processor communication no longer scales. We obtain a memory-independent communication cost lower bound on classical and Strassen-based distributed-memory matrix multiplication algorithms. These bounds imply that no classical or Strassen-based parallel matrix multiplication algorithm can strongly scale perfectly beyond the ranges already attained by the two parallel algorithms mentioned above. The memory-independent bounds and the strong scaling bounds generalize to other algorithms.
Quick Detection of Nodes with Large Degrees
Venue: arXiv cs.DS 2012
Year: 2012

Our goal is to quickly find top $k$ lists of nodes with the largest degrees in large complex networks. If the adjacency list of the network is known (not often the case in complex networks), a deterministic algorithm to find a node with the largest degree requires an average complexity of O(n), where $n$ is the number of nodes in the network. Even this modest complexity can be very high for large complex networks. We propose to use the random walk based method. We show theoretically and by numerical experiments that for large networks the random walk method finds good quality top lists of nodes with high probability and with computational savings of orders of magnitude. We also propose stopping criteria for the random walk method which requires very little knowledge about the structure of the network.
Cut-elimination for the mu-calculus with one variable
Venue: arXiv cs.LO 2012
Year: 2012

We establish syntactic cut-elimination for the one-variable fragment of the modal mu-calculus. Our method is based on a recent cut-elimination technique by Mints that makes use of Buchholz' Omega-rule.
A Tutorial of 802.11 Implementation in NS-2
Venue: arXiv cs.NI 2012
Year: 2012

By analyzing the source codes of ns-2, we discuss the simulated implementations of wireless channels, network interfaces and mostly the 802.11 MAC protocol in ns-2. We also notice the "bugs" of the 802.11 simulation compared with the reality, and present an extension to fading channels as well.
Control of Towing Kites for Seagoing Vessels
Venue: arXiv cs.SY 2012
Year: 2012

In this paper we present the basic features of the flight control of the SkySails towing kite system. After introduction of coordinate definitions and basic system dynamics we introduce a novel model used for controller design and justify its main dynamics with results from system identification based on numerous sea trials. We then present the controller design which we successfully use for operational flights for several years. Finally we explain the generation of dynamical flight patterns.
GPUs as Storage System Accelerators
Venue: arXiv cs.DC 2012
Year: 2012

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.
Generalized Boundaries from Multiple Image Interpretations
Venue: arXiv cs.CV 2012
Year: 2012

Boundary detection is essential for a variety of computer vision tasks such as segmentation and recognition. In this paper we propose a unified formulation and a novel algorithm that are applicable to the detection of different types of boundaries, such as intensity edges, occlusion boundaries or object category specific boundaries. Our formulation leads to a simple method with state-of-the-art performance and significantly lower computational cost than existing methods. We evaluate our algorithm on different types of boundaries, from low-level boundaries extracted in natural images, to occlusion boundaries obtained using motion cues and RGB-D cameras, to boundaries from soft-segmentation. We also propose a novel method for figure/ground soft-segmentation that can be used in conjunction with our boundary detection method and improve its accuracy at almost no extra computational cost.
Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks
Venue: arXiv cs.LG 2012
Year: 2012

The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. Current algorithms in this area rely on loopy belief propagation for active query selection. These algorithms have an exponential time complexity, making them slow and even intractable in large networks. We propose a rank-based greedy algorithm that sequentially chooses queries such that the area under the ROC curve of the rank-based output is maximized. The AUC criterion allows us to make a simplifying assumption that significantly reduces the complexity of active query selection (from exponential to near quadratic), with little or no compromise on the performance quality.
Semi-supervised Learning with Density Based Distances
Venue: arXiv cs.LG 2012
Year: 2012

We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.
Smoothing Proximal Gradient Method for General Structured Sparse Learning
Venue: arXiv cs.LG 2012
Year: 2012

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the l1/l2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsity-inducing penalties. Our approach is based on a general smoothing technique of Nesterov. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method.
EDML: A Method for Learning Parameters in Bayesian Networks
Venue: arXiv cs.AI 2012
Year: 2012

We propose a method called EDML for learning MAP parameters in binary Bayesian networks under incomplete data. The method assumes Beta priors and can be used to learn maximum likelihood parameters when the priors are uninformative. EDML exhibits interesting behaviors, especially when compared to EM. We introduce EDML, explain its origin, and study some of its properties both analytically and empirically.
Active Learning for Developing Personalized Treatment
Venue: arXiv cs.LG 2012
Year: 2012

The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to design more efficient trials, addressing issues such as whom to recruit, at what point in the trial, and which treatment to assign, throughout the duration of the trial. We propose a minimax bandit model with two different optimization criteria, and discuss the computational challenges and issues pertaining to this approach. We evaluate our active learning policies using both simulated data, and data modeled after a clinical trial for treating depressed individuals, and contrast our methods with other plausible active learning policies.
Inference in Probabilistic Logic Programs using Weighted CNF's
Venue: arXiv cs.AI 2012
Year: 2012

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting. To solve such tasks, we employ state-of-the-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-the-art in probabilistic logic programming.